From 37516e23941e912ce34a52dbf844b7f536904ae3 Mon Sep 17 00:00:00 2001 From: Alessandro Gasparini Date: Wed, 8 Jan 2020 16:10:13 +0100 Subject: [PATCH] Updated pkgdown site --- docs/LICENSE.html | 216 ++++++++-------- docs/articles/comorbidityscores.html | 308 ++++++++++++----------- docs/index.html | 358 +++++++++++++-------------- docs/news/index.html | 4 +- docs/pkgdown.yml | 4 +- docs/reference/comorbidity.html | 2 +- docs/reference/index.html | 16 +- docs/sitemap.xml | 8 +- 8 files changed, 457 insertions(+), 459 deletions(-) diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 9cb30ac..13981dc 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -127,10 +127,10 @@

NA

-
                GNU GENERAL PUBLIC LICENSE
-                   Version 3, 29 June 2007
+
                GNU GENERAL PUBLIC LICENSE
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Copyright (C) 2007 Free Software Foundation, Inc. http://fsf.org/ Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

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@@ -142,7 +142,7 @@

NA

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    @@ -260,28 +260,28 @@

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    @@ -340,32 +340,32 @@

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    diff --git a/docs/articles/comorbidityscores.html b/docs/articles/comorbidityscores.html index c32bbcc..3fb447e 100644 --- a/docs/articles/comorbidityscores.html +++ b/docs/articles/comorbidityscores.html @@ -79,13 +79,13 @@ -
    +

    Comorbidity scores

    Alessandro Gasparini

    -

    2019-12-26

    +

    2020-01-08

    @@ -412,160 +412,160 @@

    Examples

    The first step consists in loading the comorbidity package:

    -
    library(comorbidity)
    +
    library(comorbidity)

    We can utilise the built-in sample_diag() function to simulate ICD diagnostic codes. Both ICD-9 and ICD-10 codes are supported:

    -
    data9 <- data.frame(
-  id = sample(1:10, size = 250, replace = TRUE),
-  code = sample_diag(n = 250, version = "ICD9_2015"),
-  stringsAsFactors = FALSE
-)
-data9 <- data9[order(data9$id), ]
-data10 <- data.frame(
-  id = sample(1:10, size = 250, replace = TRUE),
-  code = sample_diag(n = 250, version = "ICD10_2011"),
-  stringsAsFactors = FALSE
-)
-data10 <- data10[order(data10$id), ]
    +
    data9 <- data.frame(
+  id = sample(1:10, size = 250, replace = TRUE),
+  code = sample_diag(n = 250, version = "ICD9_2015"),
+  stringsAsFactors = FALSE
+)
+data9 <- data9[order(data9$id), ]
+data10 <- data.frame(
+  id = sample(1:10, size = 250, replace = TRUE),
+  code = sample_diag(n = 250, version = "ICD10_2011"),
+  stringsAsFactors = FALSE
+)
+data10 <- data10[order(data10$id), ]

    Then, we can go ahead and compute various comorbidity scores and indices supported by comorbidity. The Charlson score based on ICD-9-CM data is computed as:

    -
    charlson9 <- comorbidity(x = data9, id = "id", code = "code", score = "charlson", icd = "icd9", assign0 = FALSE)
-str(charlson9)
-#> 'data.frame':    10 obs. of  22 variables:
-#>  $ id      : int  1 2 3 4 5 6 7 8 9 10
-#>  $ ami     : int  0 0 0 1 0 0 0 0 0 0
-#>  $ chf     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ pvd     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ cevd    : int  0 0 0 1 0 0 1 0 0 0
-#>  $ dementia: int  1 0 0 0 0 0 0 0 0 0
-#>  $ copd    : int  0 1 0 0 0 1 0 0 0 0
-#>  $ rheumd  : num  0 0 0 0 0 0 0 0 0 0
-#>  $ pud     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ mld     : int  1 0 1 0 0 0 0 0 0 0
-#>  $ diab    : int  1 0 0 0 0 0 0 0 0 0
-#>  $ diabwc  : int  0 0 0 0 0 1 0 0 0 0
-#>  $ hp      : num  0 0 0 0 0 0 0 0 0 0
-#>  $ rend    : num  0 0 0 0 0 0 0 0 0 0
-#>  $ canc    : int  1 1 1 1 1 1 0 1 0 0
-#>  $ msld    : num  0 0 0 0 0 0 0 0 0 0
-#>  $ metacanc: num  0 0 0 0 0 0 0 0 0 0
-#>  $ aids    : num  0 0 0 0 0 0 0 0 0 0
-#>  $ score   : num  4 2 2 3 1 3 1 1 0 0
-#>  $ index   : Factor w/ 4 levels "0","1-2","3-4",..: 3 2 2 3 2 3 2 2 1 1
-#>  $ wscore  : num  5 3 3 4 2 5 1 2 0 0
-#>  $ windex  : Factor w/ 4 levels "0","1-2","3-4",..: 4 3 3 3 2 4 2 2 1 1
-#>  - attr(*, "variable.labels")= chr  "ID" "Myocardial infarction" "Congestive heart failure" "Peripheral vascular disease" ...
    +

    The Charlson score based on ICD-10 data:

    -
    charlson10 <- comorbidity(x = data10, id = "id", code = "code", score = "charlson", icd = "icd10", assign0 = FALSE)
-str(charlson10)
-#> 'data.frame':    10 obs. of  22 variables:
-#>  $ id      : int  1 2 3 4 5 6 7 8 9 10
-#>  $ ami     : int  0 0 0 0 0 0 0 0 1 0
-#>  $ chf     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ pvd     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ cevd    : int  0 0 0 1 1 1 0 0 0 1
-#>  $ dementia: num  0 0 0 0 0 0 0 0 0 0
-#>  $ copd    : num  0 0 0 0 0 0 0 0 0 0
-#>  $ rheumd  : num  0 0 0 0 0 0 0 0 0 0
-#>  $ pud     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ mld     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ diab    : int  0 0 0 0 0 1 0 0 1 0
-#>  $ diabwc  : num  0 0 0 0 0 0 0 0 0 0
-#>  $ hp      : num  0 0 0 0 0 0 0 0 0 0
-#>  $ rend    : int  0 0 0 0 0 0 1 0 0 0
-#>  $ canc    : int  0 1 1 1 1 1 1 1 0 1
-#>  $ msld    : int  0 0 0 0 0 0 0 1 0 0
-#>  $ metacanc: int  0 0 0 0 0 0 0 1 0 0
-#>  $ aids    : num  0 0 0 0 0 0 0 0 0 0
-#>  $ score   : num  0 1 1 2 2 3 2 3 2 2
-#>  $ index   : Factor w/ 4 levels "0","1-2","3-4",..: 1 2 2 2 2 3 2 3 2 2
-#>  $ wscore  : num  0 2 2 3 3 4 4 11 2 3
-#>  $ windex  : Factor w/ 4 levels "0","1-2","3-4",..: 1 2 2 3 3 3 3 4 2 3
-#>  - attr(*, "variable.labels")= chr  "ID" "Myocardial infarction" "Congestive heart failure" "Peripheral vascular disease" ...
    +

    The Elixhauser score based on ICD-9-CM data:

    -
    elixhauser9 <- comorbidity(x = data9, id = "id", code = "code", score = "elixhauser", icd = "icd9", assign0 = FALSE)
-str(elixhauser9)
-#> 'data.frame':    10 obs. of  38 variables:
-#>  $ id         : int  1 2 3 4 5 6 7 8 9 10
-#>  $ chf        : num  0 0 0 0 0 0 0 0 0 0
-#>  $ carit      : num  0 0 0 0 0 0 0 0 0 0
-#>  $ valv       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ pcd        : int  0 1 0 0 0 0 0 0 0 0
-#>  $ pvd        : num  0 0 0 0 0 0 0 0 0 0
-#>  $ hypunc     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ hypc       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ para       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ ond        : int  0 0 0 0 1 0 1 0 0 0
-#>  $ cpd        : int  0 1 0 0 0 1 0 0 0 0
-#>  $ diabunc    : num  0 0 0 0 0 0 0 0 0 0
-#>  $ diabc      : int  1 0 0 0 0 1 0 0 0 0
-#>  $ hypothy    : int  0 0 0 0 0 0 1 0 0 0
-#>  $ rf         : num  0 0 0 0 0 0 0 0 0 0
-#>  $ ld         : int  1 0 1 0 0 0 0 0 0 0
-#>  $ pud        : num  0 0 0 0 0 0 0 0 0 0
-#>  $ aids       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ lymph      : int  1 0 1 1 0 1 0 0 0 0
-#>  $ metacanc   : num  0 0 0 0 0 0 0 0 0 0
-#>  $ solidtum   : int  0 1 0 1 1 0 0 1 0 0
-#>  $ rheumd     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ coag       : int  0 0 0 0 0 0 1 0 0 0
-#>  $ obes       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ wloss      : num  0 0 0 0 0 0 0 0 0 0
-#>  $ fed        : num  0 0 0 0 0 0 0 0 0 0
-#>  $ blane      : num  0 0 0 0 0 0 0 0 0 0
-#>  $ dane       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ alcohol    : num  0 0 0 0 0 0 0 0 0 0
-#>  $ drug       : int  0 1 1 0 0 0 1 0 0 0
-#>  $ psycho     : int  0 0 0 0 0 1 0 0 0 0
-#>  $ depre      : num  0 0 0 0 0 0 0 0 0 0
-#>  $ score      : num  3 4 3 2 2 4 4 1 0 0
-#>  $ index      : Factor w/ 4 levels "<0","0","1-4",..: 3 3 3 3 3 3 3 3 2 2
-#>  $ wscore_ahrq: num  7 9 3 13 12 1 9 7 0 0
-#>  $ wscore_vw  : num  20 4 13 13 10 12 2 4 0 0
-#>  $ windex_ahrq: Factor w/ 4 levels "<0","0","1-4",..: 4 4 3 4 4 3 4 4 2 2
-#>  $ windex_vw  : Factor w/ 4 levels "<0","0","1-4",..: 4 3 4 4 4 4 3 3 2 2
-#>  - attr(*, "variable.labels")= chr  "ID" "Congestive heart failure" "Cardiac arrhythmias" "Valvular disease" ...
    +
    elixhauser9 <- comorbidity(x = data9, id = "id", code = "code", score = "elixhauser", icd = "icd9", assign0 = FALSE)
+str(elixhauser9)
+#> 'data.frame':    10 obs. of  38 variables:
+#>  $ id         : int  1 2 3 4 5 6 7 8 9 10
+#>  $ chf        : num  0 0 0 0 0 0 0 0 0 0
+#>  $ carit      : int  0 0 0 0 0 1 0 0 0 0
+#>  $ valv       : int  0 0 0 1 0 0 0 1 0 0
+#>  $ pcd        : num  0 0 0 0 0 0 0 0 0 0
+#>  $ pvd        : num  0 0 0 0 0 0 0 0 0 0
+#>  $ hypunc     : num  0 0 0 0 0 0 0 0 0 0
+#>  $ hypc       : int  0 0 1 0 0 0 0 0 0 0
+#>  $ para       : int  1 0 0 0 0 0 0 0 0 0
+#>  $ ond        : int  1 0 0 0 0 0 0 0 0 0
+#>  $ cpd        : int  0 0 0 0 0 0 0 0 0 1
+#>  $ diabunc    : num  0 0 0 0 0 0 0 0 0 0
+#>  $ diabc      : num  0 0 0 0 0 0 0 0 0 0
+#>  $ hypothy    : num  0 0 0 0 0 0 0 0 0 0
+#>  $ rf         : int  0 0 1 0 0 0 1 0 0 1
+#>  $ ld         : num  0 0 0 0 0 0 0 0 0 0
+#>  $ pud        : num  0 0 0 0 0 0 0 0 0 0
+#>  $ aids       : num  0 0 0 0 0 0 0 0 0 0
+#>  $ lymph      : int  0 1 1 1 0 0 1 0 0 0
+#>  $ metacanc   : int  0 0 0 0 0 0 1 0 1 0
+#>  $ solidtum   : int  0 1 0 0 1 0 1 1 0 0
+#>  $ rheumd     : num  0 0 0 0 0 0 0 0 0 0
+#>  $ coag       : int  0 0 0 0 0 0 0 0 1 0
+#>  $ obes       : num  0 0 0 0 0 0 0 0 0 0
+#>  $ wloss      : num  0 0 0 0 0 0 0 0 0 0
+#>  $ fed        : num  0 0 0 0 0 0 0 0 0 0
+#>  $ blane      : num  0 0 0 0 0 0 0 0 0 0
+#>  $ dane       : num  0 0 0 0 0 0 0 0 0 0
+#>  $ alcohol    : num  0 0 0 0 0 0 0 0 0 0
+#>  $ drug       : int  0 0 0 0 1 0 0 0 0 0
+#>  $ psycho     : int  1 0 1 0 1 0 0 1 1 0
+#>  $ depre      : num  0 0 0 0 0 0 0 0 0 0
+#>  $ score      : num  3 2 4 2 3 1 4 3 3 2
+#>  $ index      : Factor w/ 4 levels "<0","0","1-4",..: 3 3 3 3 3 3 3 3 3 3
+#>  $ wscore_ahrq: num  5 13 6 6 -5 0 33 2 20 9
+#>  $ wscore_vw  : num  13 13 14 8 -3 5 30 3 15 8
+#>  $ windex_ahrq: Factor w/ 4 levels "<0","0","1-4",..: 4 4 4 4 1 2 4 3 4 4
+#>  $ windex_vw  : Factor w/ 4 levels "<0","0","1-4",..: 4 4 4 4 1 4 4 3 4 4
+#>  - attr(*, "variable.labels")= chr  "ID" "Congestive heart failure" "Cardiac arrhythmias" "Valvular disease" ...

    Finally, the Elixhauser score based on ICD-10 data:

    -
    elixhauser10 <- comorbidity(x = data10, id = "id", code = "code", score = "elixhauser", icd = "icd10", assign0 = FALSE)
-str(elixhauser10)
-#> 'data.frame':    10 obs. of  38 variables:
-#>  $ id         : int  1 2 3 4 5 6 7 8 9 10
-#>  $ chf        : num  0 0 0 0 0 0 0 0 0 0
-#>  $ carit      : num  0 0 0 0 0 0 0 0 0 0
-#>  $ valv       : int  1 0 0 0 0 0 0 0 0 0
-#>  $ pcd        : num  0 0 0 0 0 0 0 0 0 0
-#>  $ pvd        : num  0 0 0 0 0 0 0 0 0 0
-#>  $ hypunc     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ hypc       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ para       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ ond        : int  1 0 0 1 0 0 0 0 0 0
-#>  $ cpd        : num  0 0 0 0 0 0 0 0 0 0
-#>  $ diabunc    : int  0 0 0 0 0 1 0 0 0 0
-#>  $ diabc      : int  0 0 0 0 0 0 0 0 1 0
-#>  $ hypothy    : num  0 0 0 0 0 0 0 0 0 0
-#>  $ rf         : num  0 0 0 0 0 0 0 0 0 0
-#>  $ ld         : int  0 0 0 0 0 0 0 1 0 0
-#>  $ pud        : num  0 0 0 0 0 0 0 0 0 0
-#>  $ aids       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ lymph      : int  0 0 0 1 0 0 0 0 0 1
-#>  $ metacanc   : int  0 0 0 0 0 0 0 1 0 0
-#>  $ solidtum   : int  0 1 1 1 1 1 1 1 0 0
-#>  $ rheumd     : int  0 1 0 0 0 0 0 0 0 0
-#>  $ coag       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ obes       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ wloss      : int  0 0 0 0 1 0 0 0 1 0
-#>  $ fed        : num  0 0 0 0 0 0 0 0 0 0
-#>  $ blane      : num  0 0 0 0 0 0 0 0 0 0
-#>  $ dane       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ alcohol    : int  0 0 0 0 0 0 0 0 1 0
-#>  $ drug       : num  0 0 0 0 0 0 0 0 0 0
-#>  $ psycho     : num  0 0 0 0 0 0 0 0 0 0
-#>  $ depre      : num  0 0 0 0 0 0 0 0 0 0
-#>  $ score      : num  2 2 1 3 2 2 1 3 3 1
-#>  $ index      : Factor w/ 4 levels "<0","0","1-4",..: 3 3 3 3 3 3 3 3 3 3
-#>  $ wscore_ahrq: num  5 7 7 18 16 7 7 25 5 6
-#>  $ wscore_vw  : num  5 4 4 19 10 4 4 27 6 9
-#>  $ windex_ahrq: Factor w/ 4 levels "<0","0","1-4",..: 4 4 4 4 4 4 4 4 4 4
-#>  $ windex_vw  : Factor w/ 4 levels "<0","0","1-4",..: 4 3 3 4 4 3 3 4 4 4
-#>  - attr(*, "variable.labels")= chr  "ID" "Congestive heart failure" "Cardiac arrhythmias" "Valvular disease" ...
    +
    elixhauser10 <- comorbidity(x = data10, id = "id", code = "code", score = "elixhauser", icd = "icd10", assign0 = FALSE)
+str(elixhauser10)
+#> 'data.frame':    10 obs. of  38 variables:
+#>  $ id         : int  1 2 3 4 5 6 7 8 9 10
+#>  $ chf        : num  0 0 0 0 0 0 0 0 0 0
+#>  $ carit      : int  0 0 0 1 0 0 0 0 0 0
+#>  $ valv       : int  0 0 0 0 0 0 0 0 0 1
+#>  $ pcd        : num  0 0 0 0 0 0 0 0 0 0
+#>  $ pvd        : num  0 0 0 0 0 0 0 0 0 0
+#>  $ hypunc     : num  0 0 0 0 0 0 0 0 0 0
+#>  $ hypc       : num  0 0 0 0 0 0 0 0 0 0
+#>  $ para       : int  1 0 0 0 0 0 0 0 0 0
+#>  $ ond        : int  0 0 0 0 0 0 1 0 0 0
+#>  $ cpd        : int  0 0 0 0 0 0 0 0 0 1
+#>  $ diabunc    : int  0 0 0 1 0 0 0 0 0 0
+#>  $ diabc      : int  1 0 0 0 0 0 0 0 0 0
+#>  $ hypothy    : num  0 0 0 0 0 0 0 0 0 0
+#>  $ rf         : int  1 0 0 0 0 0 0 0 0 1
+#>  $ ld         : num  0 0 0 0 0 0 0 0 0 0
+#>  $ pud        : num  0 0 0 0 0 0 0 0 0 0
+#>  $ aids       : num  0 0 0 0 0 0 0 0 0 0
+#>  $ lymph      : int  1 0 0 0 0 0 0 0 1 0
+#>  $ metacanc   : int  0 0 0 0 0 0 1 0 0 0
+#>  $ solidtum   : int  0 1 1 1 0 1 0 0 0 1
+#>  $ rheumd     : int  0 0 1 0 0 0 0 1 1 0
+#>  $ coag       : num  0 0 0 0 0 0 0 0 0 0
+#>  $ obes       : num  0 0 0 0 0 0 0 0 0 0
+#>  $ wloss      : num  0 0 0 0 0 0 0 0 0 0
+#>  $ fed        : int  0 1 0 0 0 0 0 0 1 0
+#>  $ blane      : num  0 0 0 0 0 0 0 0 0 0
+#>  $ dane       : int  0 0 1 0 0 0 0 0 0 0
+#>  $ alcohol    : int  0 0 0 0 0 1 0 0 0 0
+#>  $ drug       : num  0 0 0 0 0 0 0 0 0 0
+#>  $ psycho     : int  1 0 0 0 0 0 0 0 0 0
+#>  $ depre      : int  1 0 0 0 0 0 1 0 0 0
+#>  $ score      : num  6 2 3 3 0 2 3 1 3 4
+#>  $ index      : Factor w/ 4 levels "<0","0","1-4",..: 4 3 3 3 2 3 3 3 3 3
+#>  $ wscore_ahrq: num  4 18 5 7 0 6 14 0 17 16
+#>  $ wscore_vw  : num  18 9 2 9 0 4 15 0 14 11
+#>  $ windex_ahrq: Factor w/ 4 levels "<0","0","1-4",..: 3 4 4 4 2 4 4 2 4 4
+#>  $ windex_vw  : Factor w/ 4 levels "<0","0","1-4",..: 4 4 3 4 2 3 4 2 4 4
+#>  - attr(*, "variable.labels")= chr  "ID" "Congestive heart failure" "Cardiac arrhythmias" "Valvular disease" ...

    @@ -590,16 +590,14 @@

    Contents

    Simulating ICD-10 codes

    The comorbidity packages includes a function named sample_diag() that allows simulating ICD diagnostic codes in a straightforward way. For instance, we could simulate ICD-10 codes:

    -
    # load the comorbidity package
-library(comorbidity)
-# set a seed for reproducibility
-set.seed(1)
-# simulate 50 ICD-10 codes for 5 individuals
-x <- data.frame(
-  id = sample(1:5, size = 50, replace = TRUE),
-  code = sample_diag(n = 50),
-  stringsAsFactors = FALSE
-)
-x <- x[order(x$id, x$code), ]
-print(head(x, n = 15), row.names = FALSE)
-##  id code
-##   1  B02
-##   1 B582
-##   1 I749
-##   1 J450
-##   1 L893
-##   1 Q113
-##   1  Q26
-##   1 Q978
-##   1 T224
-##   1 V101
-##   1 V244
-##   1  V46
-##   2 A665
-##   2 C843
-##   2 D838
    +

    It is also possible to simulate from two different versions of the ICD-10 coding system. The default is to simulate ICD-10 codes from the 2011 version:

    -
    set.seed(1)
-x1 <- data.frame(
-  id = sample(1:3, size = 30, replace = TRUE),
-  code = sample_diag(n = 30),
-  stringsAsFactors = FALSE
-)
-set.seed(1)
-x2 <- data.frame(
-  id = sample(1:3, size = 30, replace = TRUE),
-  code = sample_diag(n = 30, version = "ICD10_2011"),
-  stringsAsFactors = FALSE
-)
-# should return TRUE
-all.equal(x1, x2)
-## [1] TRUE
    +
    set.seed(1)
+x1 <- data.frame(
+  id = sample(1:3, size = 30, replace = TRUE),
+  code = sample_diag(n = 30),
+  stringsAsFactors = FALSE
+)
+set.seed(1)
+x2 <- data.frame(
+  id = sample(1:3, size = 30, replace = TRUE),
+  code = sample_diag(n = 30, version = "ICD10_2011"),
+  stringsAsFactors = FALSE
+)
+# should return TRUE
+all.equal(x1, x2)
+## [1] TRUE

    Alternatively, you could use the 2009 version:

    -
    set.seed(1)
-x1 <- data.frame(
-  id = sample(1:3, size = 30, replace = TRUE),
-  code = sample_diag(n = 30, version = "ICD10_2009"),
-  stringsAsFactors = FALSE
-)
-set.seed(1)
-x2 <- data.frame(
-  id = sample(1:3, size = 30, replace = TRUE),
-  code = sample_diag(n = 30, version = "ICD10_2011"),
-  stringsAsFactors = FALSE
-)
-# should not return TRUE
-all.equal(x1, x2)
-## [1] "Component \"code\": 30 string mismatches"
    +
    set.seed(1)
+x1 <- data.frame(
+  id = sample(1:3, size = 30, replace = TRUE),
+  code = sample_diag(n = 30, version = "ICD10_2009"),
+  stringsAsFactors = FALSE
+)
+set.seed(1)
+x2 <- data.frame(
+  id = sample(1:3, size = 30, replace = TRUE),
+  code = sample_diag(n = 30, version = "ICD10_2011"),
+  stringsAsFactors = FALSE
+)
+# should not return TRUE
+all.equal(x1, x2)
+## [1] "Component \"code\": 30 string mismatches"

    Simulating ICD-9 codes

    ICD-9 codes can be easily simulated too:

    -
    set.seed(2)
-x9 <- data.frame(
-  id = sample(1:3, size = 30, replace = TRUE),
-  code = sample_diag(n = 30, version = "ICD9_2015"),
-  stringsAsFactors = FALSE
-)
-x9 <- x9[order(x9$id, x9$code), ]
-print(head(x9, n = 15), row.names = FALSE)
-##  id  code
-##   1 01130
-##   1 01780
-##   1 30151
-##   1  3073
-##   1 36907
-##   1 37845
-##   1 64212
-##   1 66704
-##   1 72633
-##   1  9689
-##   1  V289
-##   2  0502
-##   2 09169
-##   2 20046
-##   2 25082
    +

    Computing comorbidity scores

    The main function of the comorbidity package is named comorbidity(), and it can be used to compute any supported comorbidity score; scores can be specified by setting the score argument, which is required.

    Say we have 3 individuals with a total of 30 ICD-10 diagnostic codes:

    -
    set.seed(1)
-x <- data.frame(
-  id = sample(1:3, size = 30, replace = TRUE),
-  code = sample_diag(n = 30),
-  stringsAsFactors = FALSE
-)
    +
    set.seed(1)
+x <- data.frame(
+  id = sample(1:3, size = 30, replace = TRUE),
+  code = sample_diag(n = 30),
+  stringsAsFactors = FALSE
+)

    We could compute the Charlson score, index, and each comorbidity domain:

    -
    charlson <- comorbidity(x = x, id = "id", code = "code", score = "charlson", icd = "icd10", assign0 = FALSE)
-charlson
-##   id ami chf pvd cevd dementia copd rheumd pud mld diab diabwc hp rend canc msld metacanc aids
-## 1  1   0   0   0    0        0    0      0   0   0    0      0  0    0    1    0        0    1
-## 2  2   0   0   0    0        0    0      0   0   0    0      0  0    0    1    0        0    0
-## 3  3   0   0   0    0        0    0      0   0   0    0      0  0    0    0    0        0    0
-##   score index wscore windex
-## 1     2   1-2      8    >=5
-## 2     1   1-2      2    1-2
-## 3     0     0      0      0
    +

    We set the assign0 argument to FALSE to not apply a hierarchy of comorbidity codes, as described in ?comorbidity::comorbidity. The default is to assume ICD-10 codes are passed to comorbidity:

    -
    charlson.default <- comorbidity(x = x, id = "id", code = "code", score = "charlson", assign0 = FALSE)
-all.equal(charlson, charlson.default)
-## [1] TRUE
    +
    charlson.default <- comorbidity(x = x, id = "id", code = "code", score = "charlson", assign0 = FALSE)
+all.equal(charlson, charlson.default)
+## [1] TRUE

    Alternatively, we could compute the Elixhauser score:

    -
    elixhauser <- comorbidity(x = x, id = "id", code = "code", score = "elixhauser", icd = "icd10", assign0 = FALSE)
-elixhauser
-##   id chf carit valv pcd pvd hypunc hypc para ond cpd diabunc diabc hypothy rf ld pud aids lymph
-## 1  1   0     0    0   0   0      0    0    0   0   0       0     0       0  0  0   0    1     0
-## 2  2   0     0    1   0   0      0    0    0   0   0       0     0       0  0  0   0    0     0
-## 3  3   0     0    0   0   0      0    0    0   1   0       0     0       0  0  0   0    0     0
-##   metacanc solidtum rheumd coag obes wloss fed blane dane alcohol drug psycho depre score index
-## 1        0        1      0    0    0     0   0     0    0       0    0      0     0     2   1-4
-## 2        0        1      0    0    0     0   0     0    0       0    0      0     0     2   1-4
-## 3        0        0      0    0    0     0   0     0    0       0    0      0     0     1   1-4
-##   wscore_ahrq wscore_vw windex_ahrq windex_vw
-## 1           7         4         >=5       1-4
-## 2           7         3         >=5       1-4
-## 3           5         6         >=5       >=5
    +

    Conversely, say we have 5 individuals with a total of 100 ICD-9 diagnostic codes:

    -
    set.seed(3)
-x <- data.frame(
-  id = sample(1:5, size = 100, replace = TRUE),
-  code = sample_diag(n = 100, version = "ICD9_2015"),
-  stringsAsFactors = FALSE
-)
    +
    set.seed(3)
+x <- data.frame(
+  id = sample(1:5, size = 100, replace = TRUE),
+  code = sample_diag(n = 100, version = "ICD9_2015"),
+  stringsAsFactors = FALSE
+)

    The Charlson and Elixhauser comorbidity codes can be easily computed:

    We could compute the Charlson score, index, and each comorbidity domain:

    -
    charlson9 <- comorbidity(x = x, id = "id", code = "code", score = "charlson", icd = "icd9", assign0 = FALSE)
-charlson9
-##   id ami chf pvd cevd dementia copd rheumd pud mld diab diabwc hp rend canc msld metacanc aids
-## 1  1   0   0   1    0        0    0      0   0   0    0      0  0    0    1    0        0    0
-## 2  2   0   0   0    1        0    0      0   0   0    0      0  0    0    0    0        0    0
-## 3  3   0   0   0    0        0    0      0   1   0    0      0  0    0    0    0        0    0
-## 4  4   0   0   1    1        0    0      0   0   0    0      0  0    0    1    0        0    0
-## 5  5   0   0   0    0        0    0      0   0   0    0      0  0    0    1    0        0    0
-##   score index wscore windex
-## 1     2   1-2      3    3-4
-## 2     1   1-2      1    1-2
-## 3     1   1-2      1    1-2
-## 4     3   3-4      4    3-4
-## 5     1   1-2      2    1-2
    +

    Alternatively, we could compute the Elixhauser score:

    -
    elixhauser9 <- comorbidity(x = x, id = "id", code = "code", score = "elixhauser", icd = "icd9", assign0 = FALSE)
-elixhauser9
-##   id chf carit valv pcd pvd hypunc hypc para ond cpd diabunc diabc hypothy rf ld pud aids lymph
-## 1  1   0     0    0   0   1      0    0    0   0   0       0     0       0  0  0   0    0     0
-## 2  2   0     0    0   0   0      0    0    0   1   0       0     0       0  0  0   0    0     0
-## 3  3   0     0    0   0   0      0    0    0   0   0       0     0       0  0  0   0    0     0
-## 4  4   0     0    0   1   1      0    0    0   0   0       0     0       0  0  0   0    0     0
-## 5  5   0     0    0   0   0      0    0    0   0   0       0     0       0  0  0   0    0     0
-##   metacanc solidtum rheumd coag obes wloss fed blane dane alcohol drug psycho depre score index
-## 1        0        0      0    0    0     0   0     0    0       0    0      0     0     1   1-4
-## 2        0        0      0    0    0     0   0     0    0       0    0      0     0     1   1-4
-## 3        0        0      0    0    0     0   0     0    0       0    0      1     0     1   1-4
-## 4        0        0      0    0    0     0   0     0    0       0    0      0     0     2   1-4
-## 5        0        0      1    0    0     0   0     0    0       0    0      0     0     1   1-4
-##   wscore_ahrq wscore_vw windex_ahrq windex_vw
-## 1           3         2         1-4       1-4
-## 2           5         6         >=5       >=5
-## 3          -5         0          <0         0
-## 4           9         6         >=5       >=5
-## 5           0         0           0         0
    +

    The weighted Elixhauser score is computed using both the AHRQ and the van Walraven algorithm (wscore_ahrq and wscore_vw).

    Citation

    If you find comorbidity useful, please cite it in your publications:

    -
    citation("comorbidity")
-## 
-## To cite the comorbidity package in publications, please use:
-## 
-##   Gasparini, (2018). comorbidity: An R package for computing comorbidity scores. Journal
-##   of Open Source Software, 3(23), 648, https://doi.org/10.21105/joss.00648
-## 
-## A BibTeX entry for LaTeX users is
-## 
-##   @Article{,
-##     author = {Alessandro Gasparini},
-##     title = {comorbidity: An R package for computing comorbidity scores},
-##     journal = {Journal of Open Source Software},
-##     year = {2018},
-##     volume = {3},
-##     issue = {23},
-##     pages = {648},
-##     doi = {10.21105/joss.00648},
-##     url = {https://doi.org/10.21105/joss.00648},
-##   }
    +

    References

    This package is based on the ICD-10-based formulations of the Charlson score and Elixhauser score proposed by Quan et al. in 2005. The ICD-9 formulation of the Charlson score is also from Quan et al. The ICD-9-based Elixhauser score is according to the AHRQ formulation (Moore et al., 2017). Weights for the Charlson score are based on the original formulation by Charlson et al. in 1987, while weights for the Elixhauser score are based on work by van Walraven et al. Finally, the categorisation of scores and weighted scores is based on work by Menendez et al. Further details on each algorithm are included in the package vignette, which you can access by typing the following in the R console:

    -
    vignette("comorbidityscores", package = "comorbidity")
    +
    vignette("comorbidityscores", package = "comorbidity")
    • Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical Care 2005; 43(11):1130-1139. DOI: 10.1097/01.mlr.0000182534.19832.83
      • diff --git a/docs/news/index.html b/docs/news/index.html index 1dafb7e..27a6761 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -180,7 +180,7 @@

      BREAKING CHANGES

      -

      Modified the behaviour of the assign0 argument after further discussion with Anders Alexandersson in Issue #9: now there is no default, forcing the user to decide whether to apply a hierarchy of comorbidity codes or not. This will make the algorithm more transparent to the end user, allowing an informed choice. See ?comorbidity::comorbidity and vignette("comorbidityscores", package = "comorbidity") for further details on the hierarchy being applied.

      +

      Modified the behaviour of the assign0 argument after further discussion with Anders Alexandersson in Issue #9: now there is no default, forcing the user to decide whether to apply a hierarchy of comorbidity codes or not. This will make the algorithm more transparent to the end user, allowing an informed choice. See ?comorbidity::comorbidity and vignette("comorbidityscores", package = "comorbidity") for further details on the hierarchy being applied.

    @@ -191,7 +191,7 @@

    BREAKING CHANGES

    comorbidity now returns two Elixhauser scores, one computed using the algorithm of val Walraven et al. (2009) and a second one computed using the AHRQ algorithm (Moore et al., 2017). Thanks to Yumiko Abe-Jones for feedback and the discussion regarding weighted Elixhauser scores.

    -

    More information can be found on the package vignette: vignette("comorbidityscores", package = "comorbidity").

    +

    More information can be found on the package vignette: vignette("comorbidityscores", package = "comorbidity").

    diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 84b726e..8c85c95 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,8 +1,8 @@ -pandoc: 2.9.1 +pandoc: 2.7.2 pkgdown: 1.4.1 pkgdown_sha: ~ articles: - comorbidityscores: comorbidityscores.html + comorbidityscores: ../../../../../../../Z:/R-Dev/comorbidity/vignettes/comorbidityscores.html urls: reference: https://ellessenne.github.io/comorbidity/reference article: https://ellessenne.github.io/comorbidity/articles diff --git a/docs/reference/comorbidity.html b/docs/reference/comorbidity.html index 6f899b0..19c95fc 100644 --- a/docs/reference/comorbidity.html +++ b/docs/reference/comorbidity.html @@ -268,7 +268,7 @@

    Value

    Labels are presented to the user when using the RStudio viewer (e.g. via the utils::View() function) for convenience.

    Details

    -

    The ICD-10 and ICD-9-CM coding for the Charlson and Elixhauser scores is based on work by Quan et al. (2005). Weights for the Charlson score are based on the original formulation by Charlson et al. in 1987, while weights for the Elixhauser score are based on work by Moore et al. and van Walraven et al. Finally, the categorisation of scores and weighted scores is based on work by Menendez et al. See vignette("comorbidityscores", package = "comorbidity") for further details on the comorbidity scores and the weighting algorithm. +

    The ICD-10 and ICD-9-CM coding for the Charlson and Elixhauser scores is based on work by Quan et al. (2005). Weights for the Charlson score are based on the original formulation by Charlson et al. in 1987, while weights for the Elixhauser score are based on work by Moore et al. and van Walraven et al. Finally, the categorisation of scores and weighted scores is based on work by Menendez et al. See vignette("comorbidityscores", package = "comorbidity") for further details on the comorbidity scores and the weighting algorithm. ICD-10 and ICD-9 codes must be in upper case and with alphanumeric characters only in order to be properly recognised; set tidy.codes = TRUE to properly tidy the codes automatically. As a convenience, a message is printed to the R console when non-alphanumeric characters are found.

    References

    diff --git a/docs/reference/index.html b/docs/reference/index.html index c1cd65a..ffdca6d 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -162,27 +162,27 @@

    icd10_2009

    +

    icd10cm_2017

    -

    ICD-10 Diagnostic Codes, 2009 Version

    +

    ICD-10-CM Diagnostic Codes, 2017 Version

    -

    icd10_2011

    +

    icd10cm_2018

    -

    ICD-10 Diagnostic Codes, 2011 Version

    +

    ICD-10-CM Diagnostic Codes, 2018 Version

    -

    icd10cm_2017

    +

    icd10_2009

    -

    ICD-10-CM Diagnostic Codes, 2017 Version

    +

    ICD-10 Diagnostic Codes, 2009 Version

    -

    icd10cm_2018

    +

    icd10_2011

    -

    ICD-10-CM Diagnostic Codes, 2018 Version

    +

    ICD-10 Diagnostic Codes, 2011 Version

    diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 20506d2..024db1a 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -13,16 +13,16 @@ https://ellessenne.github.io/comorbidity/reference/comorbidity.html - https://ellessenne.github.io/comorbidity/reference/icd10_2009.html + https://ellessenne.github.io/comorbidity/reference/icd10cm_2017.html - https://ellessenne.github.io/comorbidity/reference/icd10_2011.html + https://ellessenne.github.io/comorbidity/reference/icd10cm_2018.html - https://ellessenne.github.io/comorbidity/reference/icd10cm_2017.html + https://ellessenne.github.io/comorbidity/reference/icd10_2009.html - https://ellessenne.github.io/comorbidity/reference/icd10cm_2018.html + https://ellessenne.github.io/comorbidity/reference/icd10_2011.html https://ellessenne.github.io/comorbidity/reference/icd9_2015.html